report available

[Wellekens]

The following report is available free of charge from 

Chris.J.Wellekens, Philips Research Laboratory Brussels,
2 Avenue van Becelaere, B-1170 Brussels,Belgium.
Email wlk at prlb2.uucp

      LINKS BETWEEN MARKOV MODELS AND MULTILAYER PERCEPTRONS
                   H.Bourlard and C.J.Wellekens    
                Philips Research Laboratory Brussels

                               ABSTRACT

Hidden Markov models are widely used for automatic speech recognition.  They
inherently incorporate the sequential character of the speech signal and are
statistically trained.  However, the a priori choice of a model topology
limits the flexibility of the  HMM's. Another drawback of these models is
their weak discriminating power.

Multilayer perceptrons are now promising tools in the connectionist approach
for classification problems and have already been successfully tested on
speech recognition problems.  However, the sequential nature of the speech
signal remains difficult to handle in that kind of machine.

In this paper, a discriminant hidden Markov model is defined and it is shown
how a particular multilayer perceptron with contextual and extra feedback
input units can be considered as a general form of such Markov models.
Relations with other recurrent networks commonly used in speech recognition
are also pointed out.

                                           Chris